Predict Arrhythmia Risk Using Intelligent Software (PARIS)

Artificial Intelligence-based Prediction and Detection of Critical Arrhythmias in Acute Cardiac Illness.

Cardiac arrhythmias frequently occur in patients admitted to the Coronary Care Unit (CCU). The majority of these patients are treated for an acute myocardial infarction, which carries an increased risk of life-threatening arrhythmias such as ventricular tachycardia (VT) or ventricular fibrillation (VF). This risk is one of the reasons these patients are monitored for 48 hours after a myocardial infarction, in accordance with the guidelines of the European Society of Cardiology (ESC) for acute coronary syndrome. Other arrhythmias, such as asystole, atrial fibrillation, or atrioventricular block, also occur in CCU patients. These arrhythmias are recorded on the electrocardiogram (ECG) monitor in the CCU and trigger an alarm for healthcare staff. However, in order to apply this alarming with sufficient sensitivity, many false positive alarms are also produced, which increases the workload for nurses (alarm fatigue) and undermines patient well-being.

This study will investigate whether Artificial Intelligence (AI) models, using continuous ECG data, can help improve the prediction of patients at risk of a life-threatening cardiac arrhythmia. Firstly, this study will aim to predict patients at risk of VT/VF in both the short term (30 minutes) and long term (1 day) in patients under continuous telemetric monitoring. This prediction facilitates timely intervention by the team in the short term, and in the long term, the safe transfer of a patient to a lower-complexity ward or earlier safe discharge of a patient. Secondly, this study will aim for improved detection to reduce the number of false negative alarms and thereby reduce alarm fatigue.

The performance of these AI models can be evaluated through this retrospective observational study. Patients aged 18 years or older who have been admitted with acute cardiac disease will be included. The primary objective of this study will be to evaluate the performance of AI models that detect and predict critical arrhythmias in the short and long term, using ECG data obtained via the monitoring system.

Study Overview

• Status: Recruiting
• Conditions: Ventricular Fibrillation; Cardiac Arrhythmias; Acute Myocardial Infarction (AMI); Ventricular Tachycardia (VT)

Detailed Description

Primary objective:

Assessment of the performances of AI models in identifying patients at risk of sustained VT and VF from bedside monitor ECG in different timeframes:

• 30-minute prediction model
• 1-day prediction model

Secondary objectives:

• Assessment of potential healthcare savings if the AI model in would be used in clinical practice, such as CCU length-of-stay (CCU-LOS), hospital length-of-stay and associated costs

Exploratory objectives:

• Real time and continuous detection of events for alarming
• Prediction of other types of arrhythmias (e.g., Atrial fibrillation (AF), atrioventricular block, severe brady-arrhythmia) using in hospital ECG monitoring.
• Identification of clinical risk factors for sustained VT and/or VF
• Exploration of development and assessment of new AI models using new (clinical) input

• Study Type: Observational
• Enrollment (Estimated): 3000

Contacts and Locations

• Study Contact: Name: Maud E Kortman, M.D.; Phone Number: 040 239 9111; Email: maud.kortman@catharinaziekenhuis.nl
• Study Contact Backup: Name: Luuk C Otterspoor, Dr. M.D.; Email: luuk.otterspoor@catharinaziekenhuis.nl

Study Locations

• Netherlands
  • North Brabant
    • Eindhoven, North Brabant, Netherlands, 5623 EJ
      • Recruiting
      • Catharina Hospital Eindhoven
      • Contact: Maud E Kortman, M.D.; Phone Number: 040 239 9111; Email: maud.kortman@catharinaziekenhuis.nl
      • Principal Investigator: Luuk C Otterspoor, Dr. M.D.

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

Patients aged 18 years or older admitted to the Catharina hospital Eindhoven (CZE) with cardiac diseases from 1/1/2023.

Description

Inclusion criteria:

• Patients admitted from 1/1/2023*
• Patients aged 18 years or older
• Admitted for acute cardiac illness or after elective cardiac procedures
• Who are on ECG monitoring in the CCU, ICU or ward

• Patients for whom continuous waveform ECG data have been routinely stored.

  • Continuous waveform ECG data has been routinely stored in the CZE since 1/1/2023 on the ICU, since 1/12/2025 on the CCU and on the ward it has yet to be implemented. As our project utilizes this continuous ECG data, it will only include patients for whom this data is available.

Exclusion Criteria:

- Patients who expressed their preference for not having their data used for scientific research or to improve quality of care in the opt-out program of the CZE.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Adult patients admitted for acute cardiac illness/elective cardiac procedures on ECG monitoring

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Occurrence of sustained ventricular tachycardia or ventricular fibrillation	The primary outcome of the study is the occurrence of sustained ventricular tachycardia (VT) (monomorphic and polymorphic with a heartrate > 100 bpm and duration > 30 seconds or with hemodynamic compromise such as fainting or need for resuscitation) or ventricular fibrillation. (Binary outcome measure 0 = no event during admission, 1 = event during admission)	During admission

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Secondary outcome measure	- A 'textbook' outcome (no adverse events) (Binary outcome measure 0 = no event during admission, 1 = event during admission)	During admission
Secondary Outcome Measure	- In-hospital onset and offset of cardiac arrhythmias (e.g. atrial fibrillation, atrio-ventricular block or severe tachy- or bradyarrhythmia, non-sustained VT). (Binary outcome measure 0 = no event during admission, 1 = event during admission)	during admission
Secondary outcome measure	- In hospital death/cardiovascular in-hospital death (include cause if available) (Binary outcome measure 0 = no event during admission, 1 = event during admission)	During admission
Secondary outcome measure	- Pulseless electrical activity (PEA) and asystole (Binary outcome measure 0 = no event during admission, 1 = event during admission)	During admission
Performance of AI prediction model	Discrimination of AI prediction model expressed with Area Under the Receiver Operating Characteristic curve (AUROC), Area Under the Precision-Recall Curve (AUPRC), sensitivity, specificity, (Positive Predictive Value) PPV and (Negative Predictive Value) NPV	During admission

Collaborators and Investigators

• Sponsor: Catharina Ziekenhuis Eindhoven
• Investigators: Principal Investigator: Luuk C Otterspoor, Dr. M.D., Catharina Ziekenhuis Eindhoven

Study record dates

Study Major Dates

• Study Start (Actual): January 1, 2023
• Primary Completion (Estimated): April 1, 2029
• Study Completion (Estimated): April 1, 2029

Study Registration Dates

• First Submitted: August 12, 2026
• First Submitted That Met QC Criteria: August 26, 2026
• First Posted (Actual): August 28, 2026

Study Record Updates

• Last Update Posted (Actual): August 28, 2026
• Last Update Submitted That Met QC Criteria: August 26, 2026
• Last Verified: August 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Acute myocardial infarction; Deep Learning; Electrocardiography; Machine Learning; Ventricular Tachycardia; Prediction model; Ventricular Fibrillation; Convolutional Neural Network; CNN
• Additional Relevant MeSH Terms: Cardiac Conduction System Disease; Cardiovascular Diseases; Pathologic Processes; Heart Diseases; Tachycardia; Pathological Conditions, Signs and Symptoms; Tachycardia, Ventricular; Arrhythmias, Cardiac; Ventricular Fibrillation
• Other Study ID Numbers: nWMO-2025.186; project code 24PPS046 (Other Grant/Funding Number: National collaboration grant (Holland High Tech, Eindhoven University of Technology, Catharina Hospital Eindhoven, Phil)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Sensitive patient information, no permission to share outside of hospital

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No